Predicting Likely Receivers Throughout an NFL Play
Zachary Pipping, Lou Zhou | Karim Kassam
Motivation
Determining who the likely recipient can be key in making the optimal decision for the defene as well as evaluating the decision-making of a quarterback
Rewarding quarterbacks for finding uncommon but high-value passes
For a defense, determining likely throw target for appropriate positioning
Look to build a ranking model which determines the most likely receiver at a frame given throw attempt
Data Overview
2025 NFL Big Data Bowl – Weeks 1–9
Game Data – Home and Away Team, Final Score, Game Time
Play Data – Play Description, Game Context, Play Result, Changes in Win Probability
Player Play Data – Statistics for each player for a play
Route ran by player, Whether the player made a tackle or interception
Tracking Data - Locations of players and the football at each frame of a play
Spacing Tells an Incomplete Story
Methodology
Building a ranking algorithm(e.g. XGBoost) to rank the likeliest recipient at a frame
Similar approaches in soccer1
Extracting features from tracking and play-by-play data
Potential pre-snap work, predicting the most likely target given the receiver alignments
Next Steps
Extracting features used to build the ranking model
Distance, Relative Speed, Relative Orientation From Nearest Defender
Quarterback Position, If Under Pressure
Number of Defenders between Quarterback and Receiver, Passing Angle
Game Score, Time Remaining
Building baseline models to compare with ranking algorithm